Overview of Strategic Approach to Asset Management and Decision-Making
Bibliographic record
Abstract
This article focuses on identifying and analyzing elements of strategic asset management (SAM). It reviews physical asset management (AM) models defined as the coordinated activity of an organization to realize value from assets, along with SAM which focuses on aligning AM strategies with various levels of organizational strategy (corporate, business, and functional-level strategy). The analysis of recent scientific papers devoted to AM with a special emphasis on SAM aspects and decision-making models as well as risk management help provide a starting point for new research directions specifically the influence of risk-informed decision-making (RIDM) process and emerging industry 4.0 technologies, as well as resilience engineering on SAM. For instance, applying Maintenance 4.0 using technological solutions such as industrial internet of things (IIOT), cloud computing, cyber-physical systems (CPS), and big data analytics could decrease the challenges encountered by top management such as managerial, technological, and methodological challenges. The outcomes will contribute to position and validate the link between SAM and RIDM, as well as Maintenance 4.0 and resilience engineering inside the vast discipline of AM and the alignment with various levels of organizational strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".